This is an interview with Jensen Huang, co-founder and CEO of NVIDIA, detailing his personal journey, early life influences, career path, and the founding and evolution of NVIDIA, emphasizing its pivotal role in the development of accelerated computing and AI.
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It is August 9th, 2024 and I am Hansen
Sue with the Computer History Museum
here with Doug Fairburn, also with the
Computer History Museum. And here we're
with Jetson Huang. Thank you for coming
and uh so we'll start with uh where and
when were you born and talk about your
early years.
I was born in Taipei, Taiwan,
February 17th, 1963. Is uh the same year
that Michael Jordan was born.
It is uh the year right before the IBM
system 360 was announced.
Pretty good year.
And could you talk about growing up in
Taiwan? I was there for about five
years. Uh my my parents
uh my father
uh got a job uh to open a uh start a oil
refinery in Thailand. And so when we
were 5 years old uh we moved to Thailand
and um uh we were there for about four
years or so.
uh there was a coup and there was a fair
amount of unrest in Thailand.
We loved living in Thailand. I was I I
remember a lot about living in Thailand.
Uh people were nice, the food was great.
Um uh kind of fun growing up there. And
uh uh because of the unrest, my parents
thought it would be good for us to come
to United States. And so they sent uh my
older brother uh and I to um uh to us
and we stayed with our uncle in Tacoma,
Washington for a few months as he uh as
we found found a um a private school to
go to, a boarding school to go to. And
the boarding school happened to be in
Onita, Kentucky of all places,
Clark County,
uh, Kentucky. And and, uh, so we moved
there and we went to school there for a
couple years before we hooked up with
our parents again, uh, back in Tacoma,
Washington. You know, that was kind of
the port of entry for uh, Asians uh, for
people in Asia coming to United States,
immigrating to the United States. And so
we met up back there again. And then we
moved down to to um uh uh to Oregon
where my father found a job as a
instrumentation engineer uh building uh
building paper mills and and uh and the
beginnings of uh building fabs and
things like that. Thank you. Um so what
were your child uh childhood interests?
um hobbies, sub subjects in school, books.
books.
Uh I was when I was young, I my
interests were were uh uh school school
and sports
and I was on the swim team. Uh I was on
the table tennis team.
I play I was on the soccer team. Uh
somehow I really enjoyed sports and and
really enjoyed school. And so I did
those two things. I played a lot of
tennis, played a lot of table tennis.
Um, when I was when I was 14, I think it
was 13 or 14, I started paying table
tennis quite quite seriously. And and um
I it was in Oregon at the time and so I
played table tennis just about every
single day, seven days a week for about
three or four years. uh got quite good.
Went to the US Open, went to the US
closed tournaments and and um I that was
a that was a very big part of my life. I
really enjoyed really enjoy table
tennis. I enjoy table tennis to this
day. I don't play it, but I watch it. Um
but but um but that was it. School and sports.
sports.
And how did you come to be interested in
electronics or computers?
Well, I was always good in science and
math and and um uh that that was
probably very heavily influenced by my
father. He was an engineer and and uh he
was excellent in in science and math and
and um
you know his field is instrumentation
and chemicals and um
you know he's he's incredibly he's he's
incredible in in that that field of
science and engineering. And so I was I
was always always um uh inspired to do
the same.
And it wasn't it wasn't until in high school
school
um in a lot of ways I I would have been
I would have probably been a petroleum
engineer of all things you know if if
not for high school. And during high
school there was a computer in in our
lab of of all things. and and um
and that computer was was a teletype if
you remember connected to a mainframe
and and uh and there was a uh some kids
that were part of the uh the computer
club and the math club and the science
club. And so I I I was part of all three
clubs and and and uh turns out all three
clubs had basically the same four
people. And so and then we became good
friends. And
and so when it was time to go to go to
college, um I I went to college in the
same place that uh my best friend at the
time, Dean Verhiden, I chose to go to
Oregon State. And I thought Oregon
State's just fine. And we both enrolled
in, you know, in a in a in a field that
was very very heavy on science and math
and was engineering. And so so
electrical engineering was was the
choice that we made. Uh so between
between the the exposure to computers uh
in high school um being part of the club
that that really enjoyed science and
math and computers uh that was kind of
the the direction I chose and and it was
it was a great direction. My my parents
thought that I I could have still had a
a great career as a petroleum engineer,
but I I decided that maybe electrical
engineer and computer science would be
my my future and it turned out okay.
Um and could you talk more about um your
experiences or um influences at college
Um
well the the most the most important
events uh so so I I think that that um uh
uh
meeting meeting the people that I met in
high school led to led to uh electrical
engineering. And while I was in
electrical engineering
and I was I went to school rather young
because I I I skipped two grades and I
was uh I was a I was uh particularly
young compared to the other kids. The
electrical engineering class was
something like 250 people or so. E
fundies at Oregon State University was
boys and four girls. And um I was the
youngest kid and and I I I remembered um
uh seeing Lori, my wife, today
and we met in E Fundy's lab and um
and I I was uh I was um
fortunate enough to to be in in the same
lab team as her and
And I suggested that we ought to do
homework together, you know, every
weekend. And that was that was um you
know that was a that was a you know my
way of of asking a girl out for a date I
guess you know and so so we would so I
you know my pickup line was was uh you
want to see my homework you know and and
I I knew that that was my superpowers
and and I was good at homework and and I
I did well in school and and so every
weekend we would get together and uh
that was that was probably the the
second best thing that that's ever
happened to me, you know. So, the first
one was just choosing electrical
engineering. That was a great thing. And
it turned out that she, you know,
choosing electrical engineering put the
two of us in the same path. And and that
that uh we've been together ever since.
And and so those those two experiences
um really formed almost everything else.
Great. Uh did you have any um important
influencers such as mentors, heroes or teachers?
I've I've always found
um that learning from unusual places and
and getting advice and insight and um
inspiration uh from from just about everybody
everybody
I meet has really formed, you know, the
way I think. And um of course of course
um you know we we had we had great
teachers um uh professor Aort and Eve Fundy's
Fundy's
uh Dr. Hennessy at Stanford. Um, you
know, the most important books, some of
the most import important books that
shaped our our careers, Carver me. Um,
there there are many uh many great
teachers and and influencers in in how
we think and uh informed our career and
uh but but the things that that I found
has been the most inspirational to me
are are people who are who are making a
living. you know, they're gardeners or
or dishwashers or cooks or painters and
artists and uh they're they're striving
to make a living through great craft and
doing the best work that they can. And
and um uh uh trying to support their
families however they could. And one of
one of the one of the one of my favorite
memories is is uh when our family went
to Kyoto one year and and and
uh uh we were going through this the
famous moss garden in one of this I
think it's a silver temple and and there
was a gardener uh squatting down uh
picking picking moss and and um all the
tourists kind of walked by him
and I stopped and I was I was amazed. ed
but by what he was doing. He It was
incredibly hot in Kyoto in the summer,
completely still air. The humidity is so
heavy. Uh it's in it's intolerable and
and um all of the all of the the
tourists are are, you know, walking as
fast as they can to the shade. And and
here here's this gardener squatting down
picking picking moss. and and he he's
had a little bamboo uh picker and a
little bamboo basket and uh no hat, just
squatting down picking picking the the
moss. And I'm looking at the basket and
there's hardly anything in there. And I
I stopped to ask him what he was doing
and and his English was perfect. And he
said he said, "Um I'm taking care of my
garden." And and I said, "Uh
but it looks it looks perfect." And he
says, "No, if you look carefully,
there's there's some dead moss." And and
he's picking the dead moss. I said, "But
your garden is enormous."
And he says, "I have plenty of time."
And and that that phrase, "I have plenty
of time." I I repeat constantly. Uh
people come to me all the time, you
know, Jensen, you're you must be really
busy. I always say, I have plenty of
time. Uh we were talking earlier, I have
plenty of time. uh uh whatever I'm
working on, I have plenty of time. I
don't wear a watch because whatever I'm
doing is the most important thing at the
time. I think I think um learning
learning uh to do uh with an aspiration
for for tackling that enormous beautiful
exquisite garden one tiny piece of moss
at a time and realizing that you have
extraord you have lots of time if you
dedicate yourself to it prioritize your
life to do that uh you have plenty of
time. It's such a great uh great way to
live and and I think to this day I try I
try to live that way that you know if
you if you prioritize your life and and
you don't get all of you know you don't
pile on uh a lot of things that are in
the end not that meaningful to you. Uh
if you prioritize your life properly and
uh you you dedicate yourself to that
priority, you have plenty of time to do
uh your life's work. And so um you know
the these kind of experiences meeting
people who are who are um uh elevating
their craft whatever it is uh dedicating
their life to it working incredibly hard
uh having a having such wonderful spirit
uh doing whatever job they have. I love
that and and they they always lift me
and inspire me. Great. Thank you. Um,
talk about your first job and what did
you learn from it?
Well, my first job, my first uh regular
regularly paying job was Denny's. I was
uh my older brother got it for me. I was
incredibly shy and and uh never left the
house and didn't know how to leave the
house except, you know, with my bicycle
or something. and and um uh my older
brother was is outgoing. Uh love loves
just being with people and and uh and he
got a job as a as a dishwasher and then
became a bus boy and a waiter at Denny's
downtown Portland. And and he said, "You
know, you ought to come you ought to
come work and get a job." And uh and so
he he um
arranged for an interview for me and and
uh I you know I got a job as a
dishwasher and he taught me how to be a
dishwasher and and then I became a bus
boy. Taught me how to be a bus boy and
then became a waiter. He taught me how
to be a be a waiter and and um that was
my first job. I I learned I learned um
uh the value of hard work of course and
like everything else whether it was
dishwashing or cleaning bathrooms at
Denny's or you know busting tables or
waiting tables uh I I always did the
very best I could and and left
everything on the field you know and so
in my eight hour shift I I worked every
second of it and um I did the best best
work that I could and always left it you
know in in uh immaculate ely for for the
next for the next shift. And so uh I I
got to learn to interact with people um
became less shy. Uh uh learn how to how
to be uh in service to someone else. Um
create a great experience for them.
Um listen to complaints. Uh learning how
to deal with complaints. and and uh
although the customer is always right um
you know ultimately you're you're also
running a business at Denny's and and uh
you know finding a solution for for for
the customer and the business is is
something that I had to learn along the
way and and so I did all all that when I
was a kid and I I love that. Yeah, that
was my first job. My first uh job after
after um graduation was was AMD and I
loved that job and I had a choice of of
uh just about every semiconductor
company and I chose AMD. I was really
really quite excited about the the
bipolar microprocessors that this team
was building and and it it um got me
exposed to designing large chips and uh
my my part my part of the of the CPU was
the was the uh uh instruction decoder
and sequencer and and uh scheduling all
of the pipelines and and so that was
that was incredibly fun to do. Uh uh so
so anyways those were great jobs. Thank
you. And could you talk about your role
at LSI Logic? Um what motivated the
move? Uh what was your role there and
what you learned and your major
accomplishments there?
When I when I was interviewing for uh a
job at Oregon State University, I got a
job offer from just about everybody I
interviewed with. and and um I had a
choice between between um AMD, Intel,
LSI Logic, National
um TI, uh all just about all of the
semiconductor companies and and um uh I
chose I chose uh in the end I had to
choose between AMD and LSI. I I couldn't
exactly decide and I ended up choosing
AMD because I wanted to go work on
building a microprocessor and and and
that was a good decision. Uh, I I
learned a lot about the trade-off
between ECL, TTL, and CMOS. And it
really it really helped you uh hone your
understanding of the scaling laws and
and um uh and the trade-off between
performance and and density and um power
and uh AC power versus
DC power, um static power versus dynamic
power. Um, all of those all of those
learnings and trade-offs were happened
in one place for me. And so I learned a
lot about about about uh chip design and
trade-offs of various process
technologies and how to think about
performance and power and density and
and so that was fun. I really enjoyed
that and I so I chose to go to AMD. Uh,
LSI Logic never gave up on me. And it
turned out that one of my office mates.
She was terrific. And she went she went
to a company called LSI Logic. And when
she got there, uh, she learned from the
the people there that they try to
recruit me out of school. And so she
contacted me again said, you know,
they're we're working on some really
great things at LSI Logic. and and um
and now that I worked at AMD, I realized
uh how impactful LSI Logic was going to
be. And and and the reason for that is
because we built all of our chips by
hand at at AMD. And it was during the
time when when um EDA was becoming
uh more of more uh capable. And I
remember I remember I was working on a
on um my instruction decoder and I was I
downloaded two open source software
packages uh Espresso and Equinat.
Equinat take truth tables and and uh or
takes equations and turn it into truth
tables and Espresso optimizes that truth
table to a better more optimized truth
table. And so those two open- source
tools from from Berkeley um enabled me
to be a lot more productive. And um
although the results were uh still uh
require a lot of massaging and you have
to you know know how to use the tools,
uh the results were very impressive and
it made it possible for for me to handle
much larger designs you know by
yourself. And so so I I was I was
smitten by the idea that uh design tools
could enable us to design much larger
VLSI systems which was what LSI Logic
and another company VLSI Technologies
was doing. And so I was so excited about
that that and it wouldn't have been
possible for me to see that insight if I
hadn't worked at AMD first. And so when
I when uh they reached out to me uh
again with LS from LSI Logic I realized
the potential and the power of the
design tools to the future of design
that is it isn't just about
understanding of circuitry um it isn't
just about understand of understanding
of process but it's really about the
understanding of these tools and it's
going to enable us to design very large
systems and and that was that was a
breakthrough. LSI Logic was
revolutionary in its uh understanding of
the intersection between system design,
design tools and semiconductor design,
chip design. That that that code-design
process if you will full stack design
process really influenced how I think
about Nvidia computing today. you know
that the importance in in if you will
deep learning is not just about the
chip, it's about the system, it's about
the tools, the frameworks and the
algorithms all being worked on together
uh to achieve extraordinary results. And
so in a lot of ways, you know, my first
my intuitions about about deep learning,
its impact to the future of computer
science and the future of software was
really influenced probably by you know
what I saw at LSI Logic in the
beginnings of that. Now that led to LSI
Logic and VSI technology both both
companies really um enabled the systems
companies to become chip designers. You
know, remember the chip industry were
chip companies coming up with um offtheshelf
offtheshelf
chips that people would buy and then
they would cobble up systems. LSI Logic
and VSSI Technologies enabled systems
companies to design exactly the systems
that exactly the chips they want. And in
a lot of ways, uh systems on a chip, if
you will, concept was really uh
conceived of uh even then. and and it it
introduced me to uh um a whole lot of
companies that were building building uh
semiconductor driven, you know, custom
chip driven systems and one of them was
some micros systemystems and and um uh
it was it was through LSI Logic that I
met uh the uh founders uh Nvidia. They
were at some micros systemystems uh and
uh Andy Betosham was designing a system
that was completely based on custom
chips. Uh Chris and Curtis were building
a a uh graphics system based on custom
chips and it exposed me to a lot of
great people that were great computer
scientists working there and and
together we we uh uh we built some
amazing systems together from the
Sundragon system which was a uh at the
time a pioneering uh system for large
scale distributed computing uh to the
Lego graphics system that Chris and
Curtis worked on uh to uh the Spark
Station one that Andy Vetoshine worked
on. Uh I was involved in all of those
and and it really influenced uh how I
thought about system design and uh
inspired me about you know how what a a
computer company would become someday
and how the semiconductor industry and
the computer industry systems companies
would really fuse into one. It used to
be two industries and today if you look
at it it's one industry one giant
industry where the where the
semiconductor industry starts and ends
where the systems company system
industry computer industry starts and
end it's completely been been overlapped
and fused and so I I had the early
insight of that seeing the formative
years of that and it it uh really shaped
my career. Thank you.
Doug is going to take take over for the
next section of the interview. Thank you
very much. Okay. Thank you.
We're going to do a start and stop. Okay.
Okay.
Whenever you're ready.
Okay. So, now that we've covered the uh
sort of precursor, let's go on to the
main event that is Nvidia. And uh what
I'm going to do is focus on four or five
of the major technological changes that
Nvidia has gone through as described on
your own website and try to understand
the dynamics of what was happening at
the time and your role in sort of
shifting the company's focus from one uh
stage to the other. Uh so I'd like to
start with the founding and that is
everybody uh you know that's interviewed
you has uh you know gotten the story of
the Denny's uh discussion with uh with
your co-founders and uh I'd like to dig
a little deeper into that. They you know
when you first were in you know wanted
you know sat down for this meeting uh
did you know what the meeting was about?
Did had they already described what
their plan was or their idea was and how
did that the ideas as presented at
Denny's evolve before you got to the the
first business plan that is what was
your input into that uh into their vision?
vision?
Uh the all what we started with um in
the beginning was was uh Chris and
Curtis wanted to start wanted to leave
Sun. I mean that that was the the
impetus of of the entire conversation
and uh the question is what what company
can be built. Uh, of course, of course,
between the three of us, uh, uh,
computer graphics, computer science,
computer systems, uh, building chips,
uh, was was really, uh, at the core of
our expertise. And, uh,
you know, in our generation, uh, we were
we were the industry leading experts in
in some of these some of these areas.
And so so the question is what kind of
company can we build uh that that could
um uh attract business uh in the near
term and then what kind of company can
it become someday and some of the things
that that the philosophies that the
three of us uh believed in. There were
two there were really two camps that
that um uh two camps in in how to do how
to do computer graphics at the time. One
of them uh was was a general purpose
processors running algorithms
and um it it really it really rides on
uh CPU scaling and general purpose
processing um uh unified memory and and
the whole the whole idea being the vast
majority of computer graphics would be
software um uh rate tracing of course is
is the perfect example of that. it was
largely a software software algorithm.
Um and and then there's there was
another another
uh group and it's a rather small group
uh that is that believes that
acceleration is really uh the best for
some uh types of applications and
computer graphics would be one example
uh of an application that really wants
to have dedicated pipelines to
accelerate the processing of it. And so
and then there are some people that are
some somewhere in in between. And so the
idea of computer graphics which is a
very computationally intensive workload
and in 1993 one of the most talked about
workloads in computer science in
computing. Um that span a spectrum from
general purpose computing all the way to
accelerated computing. And and we
thought we thought that that um uh that
we could create a company that could
accelerate um design a a bespoke
pipeline uh dedicated to uh processing
pixels and that pipeline would of course
be the formation of the first
application that uh we would accelerate
called accelerated computing. During
that time uh Curtis and David Rosenthal
uh um came up with with the architecture
of the company, the architecture of our
of what is what is still largely um uh
governs a lot of the way we think about
computing today and the way we work
today. this idea that the hardware
um because has so much software on it
will always be the limiter of the
company if we don't make it an
architecture that could span multi
multiple generations.
And how do you solve this problem that
that has hardware that is bespoke to the
algorithms meaning it's it's a pipeline
not a processor with instruction sets on
the one hand um and so you're you're
heavily dedic heavily heavily um
designed um to process a particular
algorithm pipeline on the one hand on
the other hand uh survive multiple
generations as the algorithms are
changing um as the pipeline is changing
And um the chip underneath is changing.
And so that that separation between um algorithms,
algorithms,
device drivers, algorithms and the
hardware uh uh to create a virtualized
interface. We call it univer our unified
driver architecture. Basically univvice
device architecture is kind of like a
whole bunch of accelerators could be
virtualized uh with an abstraction. that
virtualization layer that that way of
thinking of our our our hardware as a
virtualized hardware was completely
revolutionary and Chris uh Chris Curtis
and David Rosenthal came up with that
idea. Um and it allowed us to uh build
multiple generations of hardware and be
compatible with the software that we're
building on top. And that observation
that software uh is a gigantic
investment that has to be uh extended
through multiple generation on the one
hand allows us to create an installed
base on the other hand um uh and for the
platform to to get better and better
over time is a concept that really uh is
associated with general purpose computing.
computing.
these ideas that are associated with
general purpose computing but not
acceleration that we brought to
acceleration and that's why we call it
accelerated computing not accelerators.
This idea was really formed in 1993.
Reasoned about um from from a a
understanding of of uh uh computing
where the investments will go uh the
interactions between software and
hardware uh dates all the way back to
1993 and 1994 during the formative years
of our company. I'm really proud of that
investment uh really proud of that that
um uh that strategy. uh Curtis, David
Rosenthal, Chris uh did a marvelous job
in in um uh in recognizing that and
inventing that fundamental found
technology foundation for our company
and that and that kind of sets the stage
really. Uh Doug, it it it set it set the
stage for us uh to evolve from a uh
first product 3D graphics accelerator company
company
to a more than one algorithm accelerated
computing company. It is all because of
that virtualization layer, that
understanding of architecture,
understanding of the interplay between
software and hardware that really made
it possible for us. But the early days
we were focused on you know what is the
application we could uh we could sell
first and the first one was uh 3D
graphics and this the strategy part that
got us off the ground was was of course
with every single company 3D graphics is
a technology
and and what product are you going to
address um uh for what market are you
going to do it uh you know how are you
going to make an impact and we we
selected something that was
That was uh also interesting to us
because we're you know we're all of the
video game generation. Um but an
observation that almost everybody would
be a gamer if the number of genres of
games would would grow and the medium
would grow and the the platform reach
would be high. Uh and so we chose uh
three things that were the perfect three
ideas for the company. 3D graphics for
video games to be taken to the market
through a PC, a personal computer. And
it was at that perfect time of Windows
95, multimedia PCs um and uh uh the
creation of of uh 3D graphics accelerators
accelerators
uh that that really brought it together.
Now, of course, at the time, at the
time, 3D graphics for for consumer
products was very hard to do because 3D
graphics is naturally very computing
intensive. On the other hand, the video
game market was non-existent and the 3D
graphics video game market was exactly
zero and Windows 95 hadn't come yet and
video was founded in 1993. And so, you
have these three circumstances with very
low probability of success converging.
And so it was it wasn't an easy thing
for our company to to be formed. And it
caused us now to think about, you know,
how do we create a uh video game market
on top of 3D graphics for the PC
industry. And so Nvidia really largely
created the PC gaming market. And to
this day, uh we're the primary
evangelist of PC gaming. uh we developed
the necessary software and go to market
and uh uh cultivated developers uh
created libraries to to enable them to
use 3D graphics hardware uh and uh uh uh
promoted the promoted the the esports
and promoted uh and evangelized the the
this uh this this whole category and
this whole industry and now PC gaming or
video games is the largest entertainment
genre in the world And it was precisely
zero in 1993 when we started. And so we
I'm I'm you know I'm I'm I'm proud of
the the company's ability today to
really create new markets. And if you if
you think about uh accelerated
computing, accelerated computing is very
different than general purpose computing
in the sense that unless you go and
create the market, there's nothing to accelerate.
accelerate.
And unless you can come up with
algorithms to accelerate it, there will
be no market.
And so our company is one of those one
of those few companies that have solved
this chicken and egg. How do we solve
this simultaneous creation of the
technology and the creation of a market?
Uh people give us a lot of credit for
the immer the the the democratization if
you will of AI computing on our platform
and give us a lot of credit for uh uh
cultivating uh the deep learning
ecosystem around the world and
celebrating the success of it and u
helping the researchers ultimately uh uh
connect the technology to uh real
applications in the marketplace and uh
really cultivating this this uh uh this
uh ecosystem which is now called the AI
revolution. Now we started that about 15
years ago and I think this the skill set
of simultaneously creating the
technology and creating the market is
something that our company has really um
uh done well and and I'm very proud of
it and we're doing it for healthcare for
uh computational biology. We're doing it
for uh for quantum computing for
classical um uh quantum classical
computers that that will be the norm in
the future. We're doing it for uh uh for
robotics uh autonomous vehicles. Each
one of these different ecosystems
uh really requires the fundamental
invention of the algorithms and the
computing stack as well as the market
itself and the ecosystems that that
turns it into a large market and and so
we're super good at that. Well,
certainly justifiably proud for the uh
the position that you've created and the
opportunities that you've uh generated
in the marketplace. Um I'd like to go
back and drill down to a couple things
because as is well documented, there
have been a few stumbles along the way,
near-death experiences in the words of
others. And uh I'd like to figure out,
you know, exactly how you got into it
and how you got out of it, so to speak.
Uh so the first one as I understand you
know you started the development of your
first product um you had some
relationship with Sega to uh use that in
a future product. uh but it turned out
that due to some changes on the part of
Microsoft uh that product didn't work
out and so you wound up in a situation
where uh you were in short supply of
cash and uh in in somewhat a desperate
situation. So
what was your role? I mean you had other
founders uh you had to you know it was a
makeorb breakak kind of uh time. what
was going through your head and what was
going through the heads of the other
founders and the employees uh that sort
of turned that around and and got you
back on your feet.
We've had we've had quite a few
near-death experiences. Um we were the
first company to to really realize that
this intersection of um 3D graphics
consumer 3D graphics at the time a
proper 3D graphics system was about
million dollars called the reality
engine at Silicon Graphics. That's a
proper 3D graphic system and it
generated images that look right and and
um uh and what we wanted to do was to
create a system that allows for the
experience of that million-doll system
but for uh a consumer product three four
$500. And so we had to consumerize the technology.
technology.
And the first the first attempt at
consumerizing that technology
uh um made it possible for us to render
beautiful beautiful images
uh with very few transistors and very
little memory. And we used four texture
mapping curved surfaces so that we
didn't have to use as much floating
point on the CPU that wasn't available
at the time to do the transform
transform and lighting. and uh uh and no
Zbuffers because uh when when memory was
$50 per megabyte, it's you know, I don't
even think it's $50 per gigabyte now,
but $50 per megabyte, uh the amount of
memory that you can use is quite
limited. And if the amount of memory you
lose you use is limited, then the
bandwidth you can get is limited. And do
so doing the Zbuffer read modify right
is too bandwidth intensive and uses too
much memory. And so so we decided to use
a uh invent an a method of rendering
beautiful images without as many
triangles curved surfaces without
zbuffers basically presorting and um uh
without without heavy read modified
rights uh associated with texture
mapping and so we use forward texturing
instead of inverse texturing.
And so so the the the three techniques
really distinguished our architecture,
graphics architecture. Um unfortunately
those three techniques were very hard
for developers. And ultimately
ultimately the thing that that I learned
in that process is that developers is
everything. If you don't if you don't
create an architecture and a computing
platform that's easy for developers that
that's rich enough for developers to
develop their ideas uh and realize their
imagination uh it is simply not going to
be adopted no matter how cost-effective
it is. And so it turns out the right
answer was triangles instead of curved
surfaces, inverse texture mapping
instead of forward texture mapping and
Zbuffers instead of no Zbuffers. We got
it exactly wrong.
And uh by the time that we were in our
second generation full end, the first
generation had plenty of challenges with
engineering execution. We learned how to
work as a team and and build chips. Um
but the second generation was was on its
way when we finally realized that the
world is really going to move towards um
the way of doing 3D graphics that we do
it today. and and um that was a very
difficult time for the company because
because they're at this point 30
different companies
that are pursuing the same vision we are
and some of them uh startups uh some of
them spun off from large companies and
so on so forth pursuing this consumer 3D
graphics marketplace. Uh meanwhile you
know we were we were uh on the wrong
path and and that was a that was a
complicated decision for the company.
Um, first of all, because we were on the
wrong path and we were also under
contract with the with Sega, the game
console company, to build their next
generation game console for them. And
so, we simultaneously had this contract
and a whole bunch of um payments that is
going to come as a result of that that's
going to sustain the company on the one
hand. On the other hand, I knew we were
going down the wrong path. And so, there
was a lot of discussion in the company.
It was it was a quite challenging. Um uh
of course very emotional because you've
now spent two and a half years doing it
the wrong way
and um uh we have obligations to a very
large partner. competitors are racing
forward. Um and and so the question is
what to do and and I think I think
during during challenging times like
that um the ball of wax of decisions and
tangled decisions
um uh can make can paralyze almost
anybody if we if we didn't if if we if
we didn't do that if we didn't do that
we didn't do that um a lot of
complicated things.
um we we came to we came to the right
decision in the final analysis because
before you think about all of the other
decisions, what about the contract? What
about money? Um let's get on the right path.
path.
Let's just get on the right path. Uh
there were questions like if we do it
the way other people do it, uh then
what's our strategy for differentiation?
Well, doing it wrong is not going to
help you differentiate. Okay. Um, and so
let's just first do it right and then
we'll figure out how to differentiate.
Let's get the company on the right path
and then let's figure out what to do
with the contract. Let's get the company
on the right path. We'll figure out the
money part of it second. And so the
realization that we have to get the
company on the right path uh with a lot
of debate, a lot of a lot of a lot of um
uh a lot of a lot of people feeling very
differently about each part of it. I
think we we uh we were we were able to
get the company grounded on let's just
focus on the right technology first and
we'll come up with all kinds of great
strategies to differentiate. Um we will
solve uh the contract issues, we'll
solve the funding issues, but it has to
start here.
And that ability to ignore all of the
consequence of the right decision and
make the right decision first started,
you know, for us in 1995. And and I I
think a lot of companies are entangled,
a lot of decisions, a lot of people are
are so concerned about the consequence
of a decision that it keeps you from
making the right decision. Make the
right decision first. The consequence
you will think about second. And so uh
it it ended up uh uh I called Maji the
CEO of Sega at the time and I told him
our circumstance. I told him that we
were going down the wrong path and if we
would have completed the project we
would have caused Sega to go down the
wrong path and and um asked to be
relieved of that contract and and then
but then I I uh um I asked them also uh
for the money as well. And the reason
for that is because without the money uh
we would be out of business and in in
his generosity and kindness uh and good
nature. He he saw it as a way um uh to
um uh he appreciated my honesty. He
appre appreciated my frankness and um
and he he appreciated my uh my
vulnerability and uh as a CEO you have
to ask for help and so and I'm sure as a
CEO he asked many other people for help
in the past and and he saw a young CEO
that he could help and without without
the generosity of Sega Nvidia wouldn't
be here today without that making that
first decision to do uh uh the
algorithms uh the new in you know
inverse texture mapping triangles and
Zbuffers and and without making that
decision, we wouldn't be here today. U
and the rest of it the rest of it is is
just engineering excellence. The fact of
the matter is um I I started the company
with two of the finest engineers on the
planet and I was surrounded by uh the
the best of the engineers and and uh
when we pivoted and we gave ourselves no
time to build uh what ultimately turned
out to be the most high performance
consumer 3D graphics processor the world
ever seen at the time. And we did it in
just about nine months because we had no
time left. Uh we changed how we designed
chips. We changed the methodology of
verification of chips. Uh we changed how
we did almost everything so that we
could squeeze in um the ability to build
something extraordinary in the short
period of time that we had. And so the
equation kind of inverted for us, Doug.
Instead of saying this is the chip we
have to build, how long is it going to
take? We started with this is how much
time we have.
and how do we build such a thing? And
and so uh we uh we broke everything down
how we did uh co-design between software
and hardware. Uh the we adopted
emulation. There was a company that was
going out of business at the time, IOS,
and I called IOS and and asked them,
"Hey, I heard about this this instrument
that you're making called emulation, so
we could emulate our software before we
even tape it out." And they said, "No,
hey, sorry. you know, we're shutting
down because uh IOS has gone out of
business. Nobody wants this emulator.
And and we said, "Hey, let's listen. You
you have do you have one we could buy
anyways?" Turned out they had one about
to go to scrap that we bought. And uh we
made it work. Uh and we became uh the
first uh chip company to emulate in the
world. And uh now we co-design through emulation.
emulation.
uh our architecture, design, emulation,
software is all co-designed before we
tape out and we we started a a way of
designing chips and complicated systems
uh and code design between software and
chips and architecture uh that the world
never seen before and so so I think I
think making getting the company in the
right path um uh and then believing that
the engineers and your people will do
extraordinary things and make
subsequently amazing decisions
completely reinvented does and so so
that first diving catch uh that first
life-threatening um uh experience for
our company uh formed NVIDIA today. The
way we do design, you know, is exactly
the way we did it then. Okay. I'd like
to uh thank you for that. That's a great
insight. Um I want to just since we're
limited on time, just focus on one other
uh transition. You know, you sounds like
you started with a broader vision than
just doing computer graphics, uh, the
more accelerated computing and, uh, and
there was a specific time in which you
sort of moved the customer focus from
not just computer graphics and gaming to
a more general scientific computing
acceleration. Uh, tell me
how did you make that decision and what
was that process like?
the journey from from computer graphics
to physics simulation. That was probably
the first the first journey uh was an
observation that that um uh computer
graphics or virtual worlds are only so
interesting when you make it beautiful.
It had it's even when you render it
beautifully, everything is static. And
so you want to bring life to it. You
want to bring physics to it. you know,
we're ultimately in the business of of
uh simulating virtual worlds.
Electromagnet magnetics uh is the first
part. Light is the first part. Um but
physics is of course uh atomic physics
is of course a very important part of
virtual worlds and and so that when we
when we started to uh when we got into
GPUs and we made it possible for us to
render images program using software uh
the next the next chapter of that was
how can we uh use software utilizing
these uh programmable engines uh to
compute physics uh from from computer
rendering ing images to processing
particle physics or fluid dynamics and
bringing bringing the virtual world to
life. And that was really the question
and we explored it with uh initially
with a language that sat on top of our
uh 32-bit floatingpoint units and uh uh
we called that CG C for uh graphics to
be able to express more complicated
algorithms uh using C instead of
programmable shading languages. So that
was our first step. Uh we learned a lot
in that process. We learned that there
was a lot of interest uh both in in our
internal consumption uh for creating
physics algorithms and bringing worlds
to life. Um as well externally people
wanted to use it for uh CT reconstruction
reconstruction
uh wanted to use it for image
processing. Um uh seismic processing was
a was surprisingly an interesting
application. Inverse physics was was
quite uh quite interesting. And so so we
we got some early feedback that this was
a good direction. And of course the
company um kept pursuing uh C for
graphics CG uh to to what it we now know
as CUDA. And at the time uh there were a
lot of innovation around how to make uh
GPUs more and more programmable. And um
uh uh some researchers inside our
company um uh came up with uh a way of
um of uh turning all of our programmable
shaders and these pipelines into
something much more uh much more general
purpose. And the concept of shared
memory and SMS and CUDA were invented at
that time. And um but anyways the
journey was really about becoming more
and more expressive to be able to
represent um uh the world of physics and
the virtual world better. It's really
about about the idea of simulation. Uh
then then from there uh we got some
early traction with some customers and
one of them was uh was um was for
seismic processing. Schlumbumberge,
Western Gico saw the potential of using
our GPUs to accelerate seismic
processing, you know, 50 100x so that
they could reduce the the the cost of
doing seismic processing on these large
farms of of CPU supercomputers. Uh and
um that gave us that gave us um uh
confidence that there might be some
future in using Nvidia GPUs and
scientific computing. Uh eventually
eventually we uh convinced um the
Oakidge National Laboratory
uh our uh one of the largest uh US open
open science uh scientific computer
scientific computing centers uh um uh to
use NVIDIA GPUs for their next
generation supercomputer. And the reason
why they chose to do it was because uh
the amount of power necessary for
scaling these supercomputers was growing exponentially.
exponentially.
and and we know that dinard scaling uh
which is related to Moors law of course
uh dinard scaling has really slowed down
and and uh they needed another way of
doing computing and so they saw the work
that we were doing with CUDA and GPU
acceleration GPU computing and they were
excited by that inspired by that and we
built the world's largest supercomputer
at its time using Nvidia GPUs which then
exposed us to all kinds of fields of
science from molecular dynamics to you
know to uh quant quantum chemistry to uh
fluids to you know from biology to
material sciences to climate sciences,
environmental sciences just enormous
reach uh of algorithms uh as a result of
that and uh you know one step after
another step uh we made our architecture
more and more generalizable without
losing its uh efficiency for
acceleration. That balance is um is one
of the artistries of our company. you
know how do you on the one hand u make
your GPU more programmable but not so
that it becomes a general purpose
programming system and so it's we we we
wrote we walk that fine line very
carefully uh so that so that we retain
the acceleration properties the you know
many X factors of acceleration and
therefore many X factors of um energy
efficiency and cost efficiency on the
one hand on the other hand, we can
explore larger and larger domains of um
acceleration and processing so that we
can create a large install base. And as
you know, computing architectures
ultimately uh reach a tipping point when
the install base is sufficiently large
and its utility is sufficiently high. Um
uh and that's never been done for
accelerated computing before. that's
never been done for anything but
anything short of a general purpose
processor before. And so we've done
something very very unique for the first
time and we've now uh reinvented
computing if you will that in the future
almost every computer will be
accelerated in some way and that
accelerated computing is really the the
basic way of doing computing going forward.
forward.
So uh it's good that you've outlined all
of these other applications and in fact
some of the earlier applications which
may not be well known or well
understood. Uh the the one that
everybody is focused on today is of
course machine learning, deep learning,
AI and so forth. So uh tell me and I'd
like to now jump to the philosophical
level. uh you know you are you know the
major player in uh in providing the
tools and and technology to uh make AI
uh pervasive uh in its many different
forms around the world. What's your
what's your personal take on the
direction AI is going? Uh there's
obviously people talk about the the
threats, the opportunities.
uh sort of how do you look at that and
you know given that you are the engine
that is fueling this uh fervor and
activity around artificial intelligence.
Well AI is fundamental
uh AI is fundamental uh for a lot of
different reasons. This is this is uh
unquestionably one of the most
significant computing transitions we've
ever seen. It's fundamental in many
ways. First uh the way that you process
software in the future is fundamentally
different now because of AI. The vast
majority of its computation is done uh
in tensors in uh linear algebra in these
neuronet networks and so the vast
majority of the computation is now done
on GPUs and so the the computing model
has changed very dramatically. The
second part is that the the software
development methodology and the software
development tools has completely
changed. Just as in the early part of
our careers when you designed a chip by
hand versus using EDA tools to design a
chip, the methodology completely
changed. How you how you thought about
the chip completely changed. Your mental
image of your chip changed. You know
when I was designing a chip at AMD u the
mental image of my chip was transistors
and gates but when I started designing
chips using uh design tools my mental
image of the my chip was language and so
the world changed uh in in the
representation of the work and the
methodology and the tools that you use
for the work the methodology completely
changed. The third is the type of things
that you can create has changed there.
There the software that that we write by
hand is impossible to handle the
diversity and the long tail of of the
world. you know the the the world's data
is unstructured data is noisy and um uh
for many types of of simulations and
predictions uh principled algorithms or
heruristics are simply uh principled
algorithms are simply can't scale
heristics are simply too brittle and so
we need to have a new way of uh writing
software that no humans can and AI is
really that method and so I've now
described three layers the computing
layer has fundamentally changed. The
software layer has fundamentally changed
and the applications the algorithms have
fundamentally changed. And so in in a
lot of ways uh this is because it's so
fundamental it's affecting every single
industry from the computer industry of
course and we're you know Nvidia is at
the epicenter of that. We're witnessing
it firsthand to every single one of the
large industries in the world that has
to apply computers to solve problems and
everything from uh healthc care of
course retail of course um you know
digital marketing of course uh uh
manufacturing, transportation, logistics
just about every single industry is in
the process being transformed as a
result of AI. how we developed the
software and what the software can
develop completely fundamentally
changed. Some of the things that that um
we also know is that on the one hand the
the potential of of productivity gains
is incredible. Uh the potential of new applications of
Uh the potential of new applications of computing expanding the the reach of
computing expanding the the reach of computing incredible. Um of course the
computing incredible. Um of course the capabilities of it uh is a is a two
capabilities of it uh is a is a two two-edged sword.
two-edged sword. And so as much as we're investing in the
And so as much as we're investing in the capabilities of AI, we have to invest in
capabilities of AI, we have to invest in uh and innovate in the safety of AI. And
uh and innovate in the safety of AI. And so you're seeing just in just really
so you're seeing just in just really really thoughtful work and really
really thoughtful work and really foundational work being done in
foundational work being done in artificial intelligence from uh the way
artificial intelligence from uh the way that you curate data. You have to use AI
that you curate data. You have to use AI to curate data because the amount of
to curate data because the amount of data that we're processing is so
data that we're processing is so gigantic. You have to use AI to curate
gigantic. You have to use AI to curate the data to train the AI. And that AI
the data to train the AI. And that AI curation process of course allows us to
curation process of course allows us to uh enhance the the data distribution on
uh enhance the the data distribution on the one hand so that we can learn from
the one hand so that we can learn from more data uh to uh pre-select uh good
more data uh to uh pre-select uh good data to um uh to expose your AI to uh to
data to um uh to expose your AI to uh to take out data that could be highly
take out data that could be highly biasing or toxic or harmful. And so
biasing or toxic or harmful. And so using AI to curate data is a whole field
using AI to curate data is a whole field of science. And I'm really delighted to
of science. And I'm really delighted to see that work. On the other extreme, uh
see that work. On the other extreme, uh using AI to fine-tune the AI, you know,
using AI to fine-tune the AI, you know, we use now AI to uh generate synthetic
we use now AI to uh generate synthetic data to fine-tune the AI so that the AI
data to fine-tune the AI so that the AI has skills that are um that are aligned
has skills that are um that are aligned with the values and the needs of the
with the values and the needs of the application. and then creating AIS that
application. and then creating AIS that guardrail the AIS. Uh guardrailing
guardrail the AIS. Uh guardrailing technology is one of the most important
technology is one of the most important areas. And so so there's a there's a
areas. And so so there's a there's a broad spectrum of innovation in the
broad spectrum of innovation in the entire AI ecosystem starting from data
entire AI ecosystem starting from data curation of curriculum curation to uh
curation of curriculum curation to uh fine-tuning and synthetic data
fine-tuning and synthetic data generation to guard railing to
generation to guard railing to governance to interpretability.
governance to interpretability. All of that all of that is all being
All of that all of that is all being developed simultaneously in research in
developed simultaneously in research in companies large uh startup companies all
companies large uh startup companies all over the world. And so I I think that
over the world. And so I I think that that the the conversation around safe AI
that the the conversation around safe AI is very healthy and it develops as a
is very healthy and it develops as a result new methodologies, new
result new methodologies, new techniques, new technologies and new
techniques, new technologies and new companies and um meanwhile we have to
companies and um meanwhile we have to innovate quickly so that we can enable
innovate quickly so that we can enable these you know all of the safe AI
these you know all of the safe AI requires AI. You know in order to guard
requires AI. You know in order to guard rail AIs you need AIs to do that and so
rail AIs you need AIs to do that and so we have to innovate AI technology as
we have to innovate AI technology as quickly as possible. This applies to uh
quickly as possible. This applies to uh languages. Uh but remember this applies
languages. Uh but remember this applies to robotics and self-driving cars. Uh
to robotics and self-driving cars. Uh you know AI is is going to is going to
you know AI is is going to is going to be in the computer but AI will also
be in the computer but AI will also leave the computer and be be in physical
leave the computer and be be in physical AI. And so there's a a large population
AI. And so there's a a large population of uh researchers and engineers working
of uh researchers and engineers working on safeguarding AI for physical AI. And
on safeguarding AI for physical AI. And so functional safety uh from the
so functional safety uh from the computing perspective, algorithm
computing perspective, algorithm perspective, diversity and redundancy,
perspective, diversity and redundancy, multimodality sensing, all of that
multimodality sensing, all of that inside out safety, outside in safety,
inside out safety, outside in safety, kind of like air traffic controllers, if
kind of like air traffic controllers, if you will, you know, just a mountain of
you will, you know, just a mountain of research that's been uh going on in in
research that's been uh going on in in creating safe AI for both language AIs
creating safe AI for both language AIs as well as uh physical AIS. And so I
as well as uh physical AIS. And so I when when you uh when you when you look
when when you uh when you when you look under the cover is where I get to see uh
under the cover is where I get to see uh the developments around the world uh you
the developments around the world uh you you'll be enthusiastic, hopeful and
you'll be enthusiastic, hopeful and quite proud of the industry doing the
quite proud of the industry doing the work that it's doing.
work that it's doing. Okay. I'd like to again sort of switch
Okay. I'd like to again sort of switch gears and uh just focus on a couple of
gears and uh just focus on a couple of personal things. That is from a you
personal things. That is from a you obviously been extremely successful in
obviously been extremely successful in growing the company to one of the
growing the company to one of the largest companies in the world from
largest companies in the world from nothing. uh how would you characterize
nothing. uh how would you characterize your
your management style in helping guide the
management style in helping guide the company? Some are described as
company? Some are described as visionaries, technical leaders, business
visionaries, technical leaders, business leaders. What how would you describe
leaders. What how would you describe your style, if you will, uh as CEO?
your style, if you will, uh as CEO? Well, the CEO is singularly responsible
Well, the CEO is singularly responsible for uh the strategy of the company.
for uh the strategy of the company. is the CEO is is not um the only person
is the CEO is is not um the only person that comes up with their strategy but
that comes up with their strategy but singularly responsible for uh
singularly responsible for uh establishing it ultimately
establishing it ultimately although uh CEOs are surrounded by
although uh CEOs are surrounded by amazing people and I am truly gifted to
amazing people and I am truly gifted to be surrounded by the people that I am
be surrounded by the people that I am and uh uh but the CEO has to set the
and uh uh but the CEO has to set the vision you know where are we going and
vision you know where are we going and how are we going to get there um and
how are we going to get there um and strategies have have uh short-term long
strategies have have uh short-term long medium-term and long range strategies
medium-term and long range strategies and and uh the CEOs the the CEO has to
and and uh the CEOs the the CEO has to be the person uh who um orchestrates
be the person uh who um orchestrates that and so so um on the one hand uh I'm
that and so so um on the one hand uh I'm surrounded by amazing people on the
surrounded by amazing people on the other hand uh they want a future that
other hand uh they want a future that that we all want to work towards and so
that we all want to work towards and so the CEO has to be the person that that
the CEO has to be the person that that does that. Um uh my management style is
does that. Um uh my management style is to simultaneously work in the future and
to simultaneously work in the future and work in the present and very hands-on.
work in the present and very hands-on. Um I I create the conditions by by uh uh
Um I I create the conditions by by uh uh which amazing people could do great
which amazing people could do great work. Um I tend not to be very
work. Um I tend not to be very operational. Uh we have we have uh
operational. Uh we have we have uh amazing operations teams and uh people
amazing operations teams and uh people are running the business uh running
are running the business uh running engineering, running operations, running
finance, running HR. I've got amazing people who are um uh operating the
people who are um uh operating the company on a day-to-day basis. So, I
company on a day-to-day basis. So, I take myself out of the day-to-day
take myself out of the day-to-day meetings and I focus my energy on two on
meetings and I focus my energy on two on two areas that the the areas that create
two areas that the the areas that create the future and the areas that solve
the future and the areas that solve problems for the company in the near
problems for the company in the near term. Uh so that we're executing our
term. Uh so that we're executing our strategy. And so between between um uh
strategy. And so between between um uh between uh my management team uh and
between uh my management team uh and myself, you know, I'm the the person
myself, you know, I'm the the person that could uh that could float, if you
that could uh that could float, if you will. I can work on I can work with the
will. I can work on I can work with the researchers one day, work with the
researchers one day, work with the engineers another day, work with the
engineers another day, work with the marketing team another day, work across
marketing team another day, work across all of those teams most days, and um
all of those teams most days, and um allows the CEO, allows me uh to be able
allows the CEO, allows me uh to be able to float wherever the company needs me.
to float wherever the company needs me. Um but at all time I'm grounded with
Um but at all time I'm grounded with with uh the realization that the company
with uh the realization that the company needs to have a vision where it's going
needs to have a vision where it's going and the company needs to have strategies
and the company needs to have strategies to execute towards that vision. And um
to execute towards that vision. And um uh and the meanwhile the world is
uh and the meanwhile the world is changing all the time. And so your
changing all the time. And so your strategy short-term, medium range, and
strategy short-term, medium range, and long-term strategies are all
long-term strategies are all shapeshifting all the time. And so the
shapeshifting all the time. And so the the shaping of the strategy, the
the shaping of the strategy, the pivoting of the strategy and its
pivoting of the strategy and its manifestation inside the company in
manifestation inside the company in organizations and people that
organizations and people that adaptation,
adaptation, if you will, sometimes people call it,
if you will, sometimes people call it, you know, when when companies do it once
you know, when when companies do it once every decade, they call it change
every decade, they call it change agents. Um when you do it every single
agents. Um when you do it every single day, you know, I call it leadership. and
day, you know, I call it leadership. and and so we literally are shaping our
and so we literally are shaping our company uh to execute a a uh evolving
company uh to execute a a uh evolving strategy in real time. And so that's a
strategy in real time. And so that's a that's kind of the the nature of our
that's kind of the the nature of our company. Our our um our management style
company. Our our um our management style is is um unorthodox in that way.
is is um unorthodox in that way. It's unconventional in that way. Um you
It's unconventional in that way. Um you know, we don't have annual plans and
know, we don't have annual plans and five-year plans. Our plans are kind of
five-year plans. Our plans are kind of evolving all the time. We have long-term
evolving all the time. We have long-term vision. uh um uh of course we have
vision. uh um uh of course we have milestones we're hitting uh but
milestones we're hitting uh but otherwise the company is is um evolving
otherwise the company is is um evolving its strategies to accommodate this
its strategies to accommodate this vision uh all the time. So great answer.
vision uh all the time. So great answer. So given that success in your
So given that success in your style and so forth, I'm sure you get
style and so forth, I'm sure you get many uh questions from other
many uh questions from other entrepreneurs for advice or whatever. So
entrepreneurs for advice or whatever. So for a a new entrepreneur looking to
for a a new entrepreneur looking to start a company, what is your uh what do
start a company, what is your uh what do you tell them?
you tell them? Well, um
Well, um for entrepreneurs that are starting
for entrepreneurs that are starting companies today, um I'm I'm a little
companies today, um I'm I'm a little reluctant to to offer too much advice.
reluctant to to offer too much advice. And there there are a couple reasons for
And there there are a couple reasons for that.
that. Um, one reason is is is um uh their
Um, one reason is is is um uh their world is different than mine. Uh, and my
world is different than mine. Uh, and my world was different than than um uh Will
world was different than than um uh Will Corgans when he was he was at he was CEO
Corgans when he was he was at he was CEO at LSI Logic. Um, uh, or Jerry Sanders
at LSI Logic. Um, uh, or Jerry Sanders when he was CEO at AMD. Uh, and so they
when he was CEO at AMD. Uh, and so they were great CEOs, but my world was
were great CEOs, but my world was different. My environment was different,
different. My environment was different, my pace was different. and and um uh how
my pace was different. and and um uh how you build a company is like building a
you build a company is like building a machine and that machinery uh should
machine and that machinery uh should take into consideration the context its
take into consideration the context its environment. Why would you build exactly
environment. Why would you build exactly the same machine when it has to go, you
the same machine when it has to go, you know, 100 miles an hour versus a machine
know, 100 miles an hour versus a machine that that wants to go one mile an hour?
that that wants to go one mile an hour? And so the the environment should govern
And so the the environment should govern should dictate uh what the the
should dictate uh what the the architecture of that machine is and the
architecture of that machine is and the properties of that machine and the
properties of that machine and the culture of that machine, the operating
culture of that machine, the operating system of that machine. And so I'm
system of that machine. And so I'm reluctant to offer too much advice
reluctant to offer too much advice because their world is very different
because their world is very different than mine. Uh second uh I also know that
than mine. Uh second uh I also know that that
that um a healthy dose of ignorance
um a healthy dose of ignorance and irreverence
and irreverence for how hard something is is an
for how hard something is is an essential quality of startup CEOs and
essential quality of startup CEOs and and founders.
and founders. you I love I love that when I go into a
you I love I love that when I go into a particular new circumstance whether
particular new circumstance whether we're building robots or building
we're building robots or building autonomous vehicles or when we were
autonomous vehicles or when we were going into deep learning and and AI my
going into deep learning and and AI my first my first thought was how hard can
first my first thought was how hard can this be well it turns out to be insanely
this be well it turns out to be insanely hard
hard and it's the reason why it hasn't been
and it's the reason why it hasn't been solved yet but I always go into it with
solved yet but I always go into it with the attitude how hard can this be and if
the attitude how hard can this be and if somebody else has done it you know or
somebody else has done it you know or somebody else will do it why shouldn't
somebody else will do it why shouldn't it be us And so how how hard can it be?
it be us And so how how hard can it be? And so I I think when I was at when I
And so I I think when I was at when I was when when the three of us first
was when when the three of us first founded the company, you know, I think
founded the company, you know, I think our attitude was was how hard could it
our attitude was was how hard could it be? Well, it turns out to be insanely
be? Well, it turns out to be insanely hard. The amount of pain and suffering
hard. The amount of pain and suffering that you know, setbacks and
that you know, setbacks and disappointments and surprises and, you
disappointments and surprises and, you know, building exactly the wrong
know, building exactly the wrong technology and having to learn something
technology and having to learn something brand new. um
brand new. um all of that you couldn't have if you
all of that you couldn't have if you would have put all of that on day one
would have put all of that on day one and real that realization I think it
and real that realization I think it would be too scary to start and so I I
would be too scary to start and so I I think one of the best things of an
think one of the best things of an entrepreneur is the concept you know
entrepreneur is the concept you know that the idea that that it can't be that
that the idea that that it can't be that hard and because they only have one
hard and because they only have one person or two people or five people
person or two people or five people you're not going to tackle anything too
you're not going to tackle anything too challenging and so you you you either
challenging and so you you you either has you either have to solve really
has you either have to solve really small problems or you have to believe
small problems or you have to believe that between the five of you, three of
that between the five of you, three of you, you you could tackle the world.
you, you you could tackle the world. It's how hard could it be? And so you
It's how hard could it be? And so you have to have superhuman
have to have superhuman mindset even though you don't have
mindset even though you don't have superhuman capabilities. and and um and
superhuman capabilities. and and um and I I I think that that for entrepreneurs
I I I think that that for entrepreneurs um I I do think that their superpower is
um I I do think that their superpower is uh is is a fair dose of ignorance and
uh is is a fair dose of ignorance and irreverence and um and this oversized
irreverence and um and this oversized um belief in themselves. Uh you know,
um belief in themselves. Uh you know, they ought to keep that for as long as
they ought to keep that for as long as they can. And then and then over time
they can. And then and then over time reality will hit you and hopefully you
reality will hit you and hopefully you have the character to to um uh survive
have the character to to um uh survive it and and ordeal through it and the
it and and ordeal through it and the cleverness to maneuver maneuver through
cleverness to maneuver maneuver through it and the friendships around you, the
it and the friendships around you, the the teams uh to uh support you along the
the teams uh to uh support you along the way and um and uh um and the company uh
way and um and uh um and the company uh with the sheer will, you know, to to uh
with the sheer will, you know, to to uh work through those challenges. You're
work through those challenges. You're going to have plenty of them and and so
going to have plenty of them and and so so um
so um Maybe that's the advice.
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